> ## Clayton, Horrillo, and Sniderman > ## The BIAT and the AMP As Measures of Racial Prejudice in Political Science: A Methodological Assessment> ## Appendix: Data analysis and visualization (W9 AMP participants only)> > > # Initial settings --------------------------------------------------------> > rm(list = ls())> library(tidyverse)> > # Read data ---------------------------------------------------------------> > panel <- read.csv("output/w9app_panel.csv") > > > # Figure 1: Correlation between AMP and IAT -------------------------------> > # Get rid of infinite values> panel$IAT_D[panel$IAT_D=="Inf"] <- NA> panel$IAT_D[panel$IAT_D=="-Inf"] <- NA> > # Destring IAT data> panel$IAT_D <- as.numeric(panel$IAT_D)> > # Run OLS regression> mod <- summary(lm(diff~IAT_D, panel))> > # Create labelling shortcuts for model attributes> beta <- round(mod$coefficients[2,1], 3)> rsq <- round(mod$r.squared, 3)> > # Position templates> xIntercept <- max(panel$diff)> yIntercept <- max(na.omit(panel$IAT_D))> > # Text to be parsed in annotation > beta_note <- paste("beta == ", beta)> rsq_note <- paste("italic(R)^2 == ", rsq)> > # IAT vs AMP scatter> iat.amp <- ggplot(panel, aes(x = IAT_D, y = diff)) ++   geom_point(alpha = 0.1) + +   geom_smooth(method = "lm",+               formula = "y ~ x") + +   geom_hline(yintercept = 0, linetype = "dashed") ++   geom_vline(xintercept = 0, linetype = "dashed") +  +   annotate("text", x = (xIntercept + 0.235), y = yIntercept, label = beta_note, color = "black", parse = TRUE, size=6) ++   annotate("text", x = (xIntercept + 0.2), y = (yIntercept-0.11), label = rsq_note, color = "black", parse = TRUE, size=6) ++   theme_light() + +   labs(x = "BIAT-D score", +        y = "AMP score")> iat.amp> ggsave("figures/FigureB1.png", iat.amp, width = 8, height = 6)